Robust tensor-based techniques for antenna array-based GNSS receivers in scenarios with highly correlated multipath components

摘要

Applications employing Global Navigation Satellite Systems (GNSS) to provide accurate positioning are subject to drastic degradation not only due to electromagnetic interference, but also due to multipath components caused by reflections and refractions in the environment. Typically, the higher the correlation between the line-of-sight (LOS) component and the remaining multipath components, the more inaccurate is positioning estimation. In the case of safety-critical systems that depend on positioning, such as autonomous driving and civil aviation, accurate positioning is essential. State-of-the-art tensor based approaches for antenna array-based GNSS receivers assume that the components are not highly correlated, implying that the measured data is a tensor whose factor matrices are full-rank. In the case of scenarios with highly correlated (clustered) multipath components, the measured data tensor has a rank-deficient factor matrix. In this paper we propose a tensor-based scheme utilizing the multilinear rank-(L-r, L-r, 1) term decomposition via generalized eigen-value decomposition (GEVD) in order to improve the time-delay estimation of the LOS component in challenging scenarios with highly correlated multipath components by exploiting the data model resulting from NLOS component clustering. (C) 2020 Elsevier Inc. All rights reserved.